Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

• URL: http://arxiv.org/abs/2405.06784v1
• Date: Fri, 10 May 2024 19:22:24 GMT
• Title: Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare
• Authors: Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang,
• Abstract summary: Foundation models (FMs) are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback. These models are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data.
• Score: 14.399086205317358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data. This approach not only enhances the capabilities of FMs in medical diagnostics and personalized treatment but also addresses critical concerns about data privacy and security in healthcare. This survey reviews the current applications of FMs in federated settings, underscores the challenges, and identifies future research directions including scaling FMs, managing data diversity, and enhancing communication efficiency within FL frameworks. The objective is to encourage further research into the combined potential of FMs and FL, laying the groundwork for groundbreaking healthcare innovations.

Related papers

• Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review [14.88874727211064]
  AI could soon become routine in clinical practice for disease diagnosis, prognosis, treatment planning, and post-treatment surveillance. Privacy concerns surrounding patient data present a major barrier to the widespread adoption of AI in medical imaging. Federated Learning (FL) offers a solution that enables organizations to train AI models collaboratively without sharing sensitive data.
  arXiv  Detail & Related papers  (2024-09-24T16:55:32Z)
• FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models [54.09244105445476]
  This study introduces a novel knowledge injection approach, FedKIM, to scale the medical foundation model within a federated learning framework. FedKIM leverages lightweight local models to extract healthcare knowledge from private data and integrates this knowledge into a centralized foundation model. Our experiments across twelve tasks in seven modalities demonstrate the effectiveness of FedKIM in various settings.
  arXiv  Detail & Related papers  (2024-08-17T15:42:29Z)
• FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection [83.54960238236548]
  FEDMEKI not only preserves data privacy but also enhances the capability of medical foundation models. FEDMEKI allows medical foundation models to learn from a broader spectrum of medical knowledge without direct data exposure.
  arXiv  Detail & Related papers  (2024-08-17T15:18:56Z)
• Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation [15.63535423357971]
  Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving disease diagnoses, treatment planning, and post-treatment monitoring. Privacy concerns surrounding patient data hinder the assembly of large training datasets needed for developing and training accurate, robust, and generalizable models. Federated Learning (FL) emerges as a compelling solution, enabling organizations to collaborate on ML model training by sharing model training information (gradients) rather than data (e.g., medical images)
  arXiv  Detail & Related papers  (2024-06-18T17:35:52Z)
• Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review [2.710010611878837]
  Federated Learning (FL) enables multiple healthcare institutions to collaboratively learn from decentralized data without sharing it. FL's scope in healthcare covers areas such as disease prediction, treatment customization, and clinical trial research. implementing FL poses challenges, including model convergence in non-IID data environments, communication overhead, and managing multi-institutional collaborations.
  arXiv  Detail & Related papers  (2024-05-22T16:59:50Z)
• Progress and Opportunities of Foundation Models in Bioinformatics [77.74411726471439]
  Foundations models (FMs) have ushered in a new era in computational biology, especially in the realm of deep learning. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. Review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases.
  arXiv  Detail & Related papers  (2024-02-06T02:29:17Z)
• Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare [13.176351544342735]
  This paper develops an integrated framework that addresses data privacy and regulatory compliance challenges. It includes the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and substantiating the efficacy of the proposed approach.
  arXiv  Detail & Related papers  (2023-06-28T17:00:32Z)
• Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions [9.662980267339375]
  We focus on medical applications of acFL, particularly in the context of global cancer diagnosis. Recent developments in acFL have made it possible to train complex machine-learned models in a distributed manner.
  arXiv  Detail & Related papers  (2022-08-05T21:41:15Z)
• Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases [57.90226879210227]
  FedCy is a semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases.
  arXiv  Detail & Related papers  (2022-03-14T17:44:53Z)
• MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation [110.31526448744096]
  We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
  arXiv  Detail & Related papers  (2021-09-29T18:09:41Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.